import tensorflow as tf
from numpy.random import RandomState
batch_size=8
x=tf.placeholder(tf.float32,(None,2),'x-input')
y_=tf.placeholder(tf.float32,(None,1),'y-input')

w1=tf.Variable(tf.random_normal([2,1],1,seed=1))
y=tf.matmul(x,w1)

loss_less=10
loss_more=1
loss=tf.reduce_sum(tf.where(tf.greater(y,y_),(y-y_)*loss_more,(y_-y)*loss_less))
train_step=tf.train.AdamOptimizer(0.001).minimize(loss)

rdm=RandomState(1)
dataset_size=128
X=rdm.rand(dataset_size,2)
Y=[[x1+x2+rdm.rand()/10.0-0.05] for (x1,x2) in X]

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    steps=5000
    for i in range(steps):
        start=(i*batch_size)%dataset_size
        end=min(start+batch_size,dataset_size)
        sess.run(train_step,feed_dict={x:X[start:end],y_:Y[start:end]})
    print(sess.run(w1))